Spaces:
Running
Running
zhouxiangxin1998
commited on
Commit
β’
8c31b30
1
Parent(s):
3e140f1
datatype=
Browse files
app.py
CHANGED
@@ -62,7 +62,7 @@ with demo:
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pd.read_csv('data/inverse_folding.csv'),
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height=99999,
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interactive=False,
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-
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)
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with gr.TabItem("π Structure Design Leaderboard", elem_id='structure-design-table', id=1,):
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with gr.Row():
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@@ -70,7 +70,7 @@ with demo:
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pd.read_csv('data/structure_design.csv'),
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height=99999,
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interactive=False,
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-
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)
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with gr.TabItem("π Sequence Design Leaderboard", elem_id='sequence-design-table', id=2,):
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with gr.Row():
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@@ -78,7 +78,7 @@ with demo:
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pd.read_csv('data/sequence_design.csv'),
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height=99999,
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interactive=False,
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-
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)
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with gr.TabItem("π Sequence-Structure Co-Design Leaderboard", elem_id='co-design-table', id=3,):
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with gr.Row():
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@@ -86,7 +86,7 @@ with demo:
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pd.read_csv('data/co_design.csv'),
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height=99999,
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interactive=False,
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)
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with gr.TabItem("π Motif Scaffolding Leaderboard", elem_id='motif-scaffolding-table', id=4,):
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with gr.Row():
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@@ -94,7 +94,7 @@ with demo:
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pd.read_csv('data/motif_scaffolding.csv'),
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height=99999,
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interactive=False,
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)
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with gr.TabItem("π Antibody Design Leaderboard", elem_id='antibody-design-table', id=5,):
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with gr.Row():
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@@ -109,7 +109,7 @@ with demo:
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pd.read_csv('data/protein_folding.csv'),
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height=99999,
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interactive=False,
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-
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)
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with gr.TabItem("π
Multi-State Prediction Leaderboard", elem_id='multi-state-prediction-table', id=7,):
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with gr.Row():
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@@ -117,7 +117,7 @@ with demo:
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pd.read_csv('data/multi_state_prediction.csv'),
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height=99999,
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interactive=False,
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-
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)
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with gr.TabItem("π
Conformation Prediction Leaderboard", elem_id='conformation-prediction-table', id=8,):
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with gr.Row():
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@@ -125,7 +125,7 @@ with demo:
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pd.read_csv('data/conformation_prediction.csv'),
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height=99999,
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interactive=False,
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-
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)
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pd.read_csv('data/inverse_folding.csv'),
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height=99999,
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interactive=False,
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+
datatype=['markdown', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number'],
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)
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with gr.TabItem("π Structure Design Leaderboard", elem_id='structure-design-table', id=1,):
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with gr.Row():
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pd.read_csv('data/structure_design.csv'),
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height=99999,
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interactive=False,
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+
datatype=['markdown', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number'],
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)
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with gr.TabItem("π Sequence Design Leaderboard", elem_id='sequence-design-table', id=2,):
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with gr.Row():
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pd.read_csv('data/sequence_design.csv'),
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height=99999,
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interactive=False,
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+
datatype=['markdown', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number'],
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)
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with gr.TabItem("π Sequence-Structure Co-Design Leaderboard", elem_id='co-design-table', id=3,):
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with gr.Row():
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pd.read_csv('data/co_design.csv'),
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height=99999,
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interactive=False,
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+
datatype=['markdown', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number'],
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)
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with gr.TabItem("π Motif Scaffolding Leaderboard", elem_id='motif-scaffolding-table', id=4,):
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with gr.Row():
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pd.read_csv('data/motif_scaffolding.csv'),
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height=99999,
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interactive=False,
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+
datatype=['markdown', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number'],
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)
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with gr.TabItem("π Antibody Design Leaderboard", elem_id='antibody-design-table', id=5,):
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with gr.Row():
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pd.read_csv('data/protein_folding.csv'),
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height=99999,
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interactive=False,
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+
datatype=['markdown', 'number', 'number', 'number', 'number', 'number', 'number', 'number'],
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)
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with gr.TabItem("π
Multi-State Prediction Leaderboard", elem_id='multi-state-prediction-table', id=7,):
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with gr.Row():
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pd.read_csv('data/multi_state_prediction.csv'),
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height=99999,
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interactive=False,
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+
datatype=['markdown', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number'],
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)
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with gr.TabItem("π
Conformation Prediction Leaderboard", elem_id='conformation-prediction-table', id=8,):
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with gr.Row():
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pd.read_csv('data/conformation_prediction.csv'),
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height=99999,
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interactive=False,
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+
datatype=['markdown', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number', 'number'],
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)
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